Resolving polarity of hosted data streams

Abstract

Disclosed are systems and methods that that automatically classify, filter, and reduce large volumes of hosted content data using artificial intelligence technology. The aggregated hosted content data is reduced by representing the hosted content data as sets of data polarity identifiers or data polarity values that correspond to one or more sequencing identifiers that are displayed on a graphical user interface. Hosted content data packets are segmented by labeling the hosted content data packets with a sequencing identifier. The hosted content data packets are processed utilizing neural network technology to classify the hosted content data according to a polarity identifier, polarity value, sentiment identifier, or one or more subject identifiers.

What is claimed is: 
     
       1. A system for resolving attributes of hosted content data, the system comprising:
 a first computing device, wherein the first computing device comprises one or more integrated software applications that perform operations comprising: 
 (a) transmitting by a Remote Host application programming interface (API), a remote content data request to a remote hosting provider, wherein the remote content data request comprises (i) an enterprise identifier, and (ii) a requested content range; 
 (b) receiving by the Remote Host API, in response to the content data request, one or more hosted content data packets, wherein each of the one or more hosted content data packets comprises (i) user content data, and (ii) sequencing data that falls within the requested content range; 
 (c) receiving by a reduction software service, (i) the one or more hosted content data packets, and (ii) enterprise content parameter data comprising one or more sequencing identifiers that each represent a sequencing range; 
 (d) executing by the reduction software service, operations comprising
 (i) determining whether each of the one or more hosted content data packets falls within a sequencing range by processing the sequencing identifiers and the sequencing data for each of the one or more hosted content data packets, 
 (ii) labeling each of the one or more hosted content data packets with at least one of the sequencing identifiers when the one or more hosted content data packets fall within at least one sequencing ranges, 
 (iii) performing a polarity analysis that (A) processes the user content data for each of the one or more hosted content data packets labeled with at least one of the sequencing identifiers, and (B) outputs a polarity identifier for each of the one or more sequencing identifiers. 
 
 
     
     
       2. The system of  claim 1 , wherein:
 (a) the polarity analysis further generates a polarity score for each of the one or more sequencing identifiers; 
 (b) the sequencing identifiers each correspond to a time period; 
 (c) the polarity identifiers, polarity scores, and the sequencing identifiers are transmitted to an end user computing device for display on a polarity explorer graphical user interface, wherein the polarity explorer graphical user interface displays each of the one or more sequencing identifiers with the associated polarity identifier and the associated polarity score. 
 
     
     
       3. The system of  claim 1 , wherein:
 (a) the reduction software service comprises at least one neural network; and 
 (b) the at least one neural network is used to perform the polarity analysis. 
 
     
     
       4. The system of  claim 3 , wherein the at least one neural network comprises a convolutional neural network having at least three intermediate layers. 
     
     
       5. The system of  claim 4 , wherein the at least one neural network comprises a recurrent neural network having at least three intermediate layers. 
     
     
       6. The system of  claim 5 , wherein the at least one recurrent neural network comprises a long short-term memory neural network architecture. 
     
     
       7. The system of  claim 1 , wherein the first computing performs further operations comprising executing a subject analysis that (A) processes the user content data for each of the one or more hosted content data packets labeled with at least one of the sequencing identifiers, and (B) generates at least one subject identifier for each of the one or more sequencing identifiers. 
     
     
       8. The system of  claim 7 , wherein:
 (a) the reduction software service comprises a first neural network that is utilized to execute the subject classification analysis; and 
 (b) the reduction software service comprises a second neural network that is utilized to execute the polarity analysis. 
 
     
     
       9. The system of  claim 1 , wherein the first computing device performs further operations comprising executing a sentiment analysis that (A) processes the user content data for each of the one or more hosted content data packets labeled with at least one of the sequencing identifiers, and (B) generates at least one sentiment identifier for each of the one or more sequencing identifiers. 
     
     
       10. The system of  claim 1 , wherein:
 (a) the one or more sequencing identifiers each represent a time period; 
 (b) the one or more polarity identifiers and the one or more sequencing identifiers are transmitted to an end user computing device for display; and 
 (c) the end user computing device generates a polarity explorer graphical user interface (GUI) that is displayed on a display screen of the end user computing device, wherein the polarity explorer GUI displays the one or more polarity identifiers each associated with a time period. 
 
     
     
       11. A system for resolving attributes of hosted content data, the system comprising:
 a first computing device, wherein the first computing device comprises one or more integrated software applications that perform operations comprising: 
 (a) transmitting by a Remote Host application programming interface (API), a remote content data request to a remote hosting provider, wherein the remote content data request comprises (i) an enterprise identifier, and (ii) a requested content range; 
 (b) receiving by the Remote Host API, in response to the content data request, one or more hosted content data packets, wherein each of the one or more hosted content data packets comprises (i) user content data, and (ii) sequencing data that falls within the requested content range; 
 (c) executing by a reduction software service, a segmentation operation that utilizes enterprise content parameter data comprising one or more sequencing identifiers that each represent a sequencing range, wherein executing the segmentation operation comprises the steps of
 (i) determining whether each of the one or more hosted content data packets falls within a sequencing range by processing the sequencing identifiers and the sequencing data for each of the of the one or more hosted content data packets, and 
 (ii) labeling the one or more hosted content data packets with at least one of the sequencing identifiers when the one or more hosted content data packets falls within at least one sequencing range; and 
 
 (d) receiving by a reduction software service, the one or more hosted content data packets labeled with at least one sequencing range; 
 (e) executing by the reduction software service, a polarity analysis that (i) processes the user content data for each of the one or more hosted content data packets labeled with at least one sequencing identifier, and (ii) generates a polarity identifier for each sequencing identifier; and 
 (f) executing by the reduction software service, a subject classification analysis that (i) processes the user content data for each for each of the one or more hosted content data packets labeled with at least one sequencing identifier, and (ii) generates at least one subject identifier for each sequencing identifier. 
 
     
     
       12. The system of  claim 11 , wherein:
 (a) the reduction software service comprises at least one neural network; and 
 (b) the at least one neural network is used to perform the polarity analysis. 
 
     
     
       13. The system of  claim 12 , wherein, the at least one neural network comprises a convolutional neural network having at least three intermediate layers. 
     
     
       14. The system of  claim 11 , wherein:
 (a) the reduction software service comprises a first neural network that is utilized to execute the subject classification analysis; and 
 (b) the reduction software service comprises a second neural network that is utilized to execute the polarity analysis. 
 
     
     
       15. The system of  claim 11 , wherein the first computing device performs further operations comprising executing a sentiment analysis that (A) processes the user content data for each of the one or more hosted content data packets labeled with at least one of the sequencing identifiers, and (B) generates at least one sentiment identifier for each of the one or more sequencing identifiers. 
     
     
       16. The system of  claim 11 , wherein:
 (a) the one or more sequencing identifiers each represent a time period; 
 (b) the one or more polarity identifiers and the one or more sequencing identifiers are transmitted to an end user computing device for display; and 
 (c) the end user computing device generates a polarity explorer graphical user interface (GUI) that is displayed on a display screen of the end user computing device, wherein the polarity explorer GUI displays the one or more polarity identifiers each associated with a time period. 
 
     
     
       17. A computer-implemented method for resolving attributes of messaging data, the method comprising:
 (a) transmitting to a remoting hosting provider technology platform, a remote content data request comprising (i) an enterprise identifier, and (ii) a requested content range; 
 (b) receiving in response to the content data request, one or more hosted content data packets, wherein each of the one or more hosted content data packets comprises (i) user messaging data, and (ii) sequencing data that falls within the requested content range; 
 (c) executing a segmentation operation utilizing enterprise content parameter data that comprises one or more time period identifiers that each represent a time period range, wherein executing the segmentation operation comprises the steps of
 (i) determining whether each of the one or more hosted content data packets falls within a time period range by processing the time period identifiers and the sequencing data for each of the of the one or more hosted content data packets, and 
 (ii) labeling the one or more hosted content data packets with at least one of the time period identifiers when the one or more hosted content data packets fans within at least one of the time period ranges; and 
 
 (d) performing a polarity analysis that (A) processes the user messaging data for each of the one or more hosted content data packets labeled with at least one of the time period identifiers, and (B) outputs a polarity identifier and a polarity score for each of the one or more time period identifiers. 
 
     
     
       18. The computer-implemented method of  claim 17  comprising the further operations of:
 (a) transmitting the one or more polarity identifiers and the one or more sequencing identifiers to an end user computing device for display; and 
 (b) generating by the end user computing device, a polarity explorer graphical user interface (GUI) that is displayed on a display screen of the end user computing device, wherein the polarity explorer GUI displays the one or more polarity identifiers each associated with a time period. 
 
     
     
       19. The computer-implemented method of  claim 18 , wherein:
 (a) a neural network is utilized to perform the polarity analysis; and 
 (b) the neural network comprises a convolutional neural network having at least three layers. 
 
     
     
       20. The computer-implemented method of  claim 18  comprising the further operations of executing a subject analysis that (i) processes the user content data for each of the one or more hosted content data packets labeled with at least one of the sequencing identifiers, and (ii) generates at least one subject identifier for each of the one or more sequencing identifiers.